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Clinical Trial Details — Status: Active, not recruiting

Administrative data

NCT number NCT05682105
Other study ID # AEHD-2022
Secondary ID
Status Active, not recruiting
Phase
First received
Last updated
Start date December 1, 2018
Est. completion date June 30, 2023

Study information

Verified date January 2023
Source Sun Yat-sen University
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases.


Description:

This study demonstrated that deep learning models could detect jaundice using ocular images in blood levels with reasonable accuracy, providing a non-invasive method for jaundice detection and recognition. This algorithm can assist clinical surgeons with daily follow-up visits and provide referral advice. It also highlights the algorithm's potential smartphone application in sizeable real-world population-based disease-detecting or telemedicine programs.


Recruitment information / eligibility

Status Active, not recruiting
Enrollment 1633
Est. completion date June 30, 2023
Est. primary completion date October 30, 2022
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - The quality of slit-lamp images should be clinical acceptable. More than 90% of the slit-lamp image area, including three central regions (sclera, pupil, and lens) are easy to read and discriminate. Exclusion Criteria: - Images with light leakage (>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis

Study Design


Locations

Country Name City State
China Zhongshan Ophthalmic Center Guangzhou Guangdong

Sponsors (4)

Lead Sponsor Collaborator
Sun Yat-sen University Affiliated Huadu Hospital of Southern Medical University, Aikang Health Care, Third Affiliated Hospital, Sun Yat-Sen University

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary area under the receiver operating characteristic curve of the deep learning system The investigators will calculate the area under the receiver operating characteristic curve of deep learning system baseline
Secondary sensitivity and specificity of the deep learning system The investigators will calculate the sensitivity and specifity of deep learning system baseline
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